New Signal Identification Algorithms for Enhanced Gamma-Ray Burst Detection in the Advanced Particle-Astrophysics Telescope

This work presents a series of algorithmic advancements aimed at improving photon signal identification and gamma-ray burst (GRB) source localization for the Advanced Particle-astrophysics Telescope (APT) and its Antarctic Demonstrator (ADAPT). These advancements are aimed at identifying valid signals in noisy environments. Previous methods failed to effectively distinguish real photon signals from noise, prompting us to develop a new photon detection algorithm with peak counting. Instead of integrating all waveform data in the observation window, we use multiple thresholds to accurately identify single-photon and two-photon arrival events, minimizing false counts due to amplifier noise. The new peak count algorithm also incorporates extra logic to avoid repeated counting and improve accuracy. Additionally, we address challenges in optimizing the integration window to mitigate dark count effects. Our strategy is informed by analysis based on the distinct statistical distributions of real photons and dark counts. We found the minimum standard deviation integration window for different numbers of photon arrival events. In the digitizer stage, a new filtering mechanism ensures events possess enough active detector layers to enhance Compton reconstruction performance. Raw sensor signals are formed into ``hit groups'' that represent individual gamma-ray interactions in the scintillator. Previous algorithms depend upon multiple thresholds for forming hit groups. To further improve hit group detection, especially under high zero threshold 2 constraints, we introduce the concept of an ``extra hit group." We only implement this extra hit group when we can not identify a normal hit group that only uses adjacent data. Combining non-adjacent data into an extra hit group when no normal hit group is identified reduces the loss of real photon events. These combined enhancements contribute to more robust and accurate localization of GRB sources, especially when dealing with background noise.